In the Business Intelligence (BI) computer applications domain, business decision makers use analytical software to pose operational performance questions as queries against multi-dimensionally modeled business databases and data warehouses. These multi-dimensional models and analysis software tools are based on Online Analytic Processing (OLAP) concepts and technology. The analysis activity typically involves the creation and manipulation of reports.
Large OLAP databases and multi-dimensionally modeled data warehouses typically contain large numbers of dimensional members or flat/non-existent dimensional hierarchies, or both. This is due to a variety of factors, including the volume of available and important data as a business operates and grows, the time constraints and computing resources required to stage and model the data warehouse and make it available for business decision-making processes, the need for flexible, unconstrained models for key business dimensions such as Customers and Time, or non-hierarchical models for inherently parent-child-relationship dimensions such as Invoices and Orders.
When a user is working with a large set of members from an OLAP database, the user can be overwhelmed by the details stored within the database. Thus, it is desirable for the user to be able to break the set up into discrete pieces and then to work with those pieces as conceptual objects.
There exists a technique that provides a simplistic split of a set of members into a subset of members about which the user cares (included) and a subset of members about which the user does not care (excluded).
Also, some data sources include corporate standard segmentations. Corporate standard segmentations are segmentation concepts that are defined by the business model of a company or subset of the company. These segments are typically well-understood by all users. An example of corporate standard segmentations is between legacy products and current products.
However, these existing segmentation mechanisms do not provide sufficient flexibilities in some situations.
It is therefore desirable to provide a mechanism for improved segmentation management.